Assignment 6
Kyle Walker
10/9/2019
Assignment 6. Data Visualization with ggplot2
Submission Instruction. You will need to submit on Blackboard, in the Assignment section, a link to your assignment on your Github Webpage. Following this to create a github webpage and posted a link to your assignment.
library(tidyverse)## -- Attaching packages ---------------------------------------------- tidyverse 1.2.1 --## v ggplot2 3.2.1 v purrr 0.3.2
## v tibble 2.1.3 v dplyr 0.8.3
## v tidyr 1.0.0 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.4.0## -- Conflicts ------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(readxl)
library(stringr)
library(gganimate)
c2015 <- read_excel("C:/Users/student/Documents/Senior Year/MATH 421/Assignment 2/c2015.xlsx")
#Fixing Sex
c2015$SEX[is.na(c2015$SEX)]<- "Female"
#Fixing Age
c2015$AGE[c2015$AGE == 'Less than 1'] <- "0"
c2015$AGE <- as.numeric(c2015$AGE)## Warning: NAs introduced by coercionc2015$AGE[is.na(c2015$AGE)] <- mean(c2015$AGE)
#Fixing Trav_Sp
c2015$TRAV_SP <- str_replace(c2015$TRAV_SP, " MPH", "")
c2015$TRAV_SP <- str_replace(c2015$TRAV_SP, "Not Rep", "")
c2015$TRAV_SP <- str_replace(c2015$TRAV_SP, "Unknown", "")
c2015$TRAV_SP <- as.numeric(c2015$TRAV_SP)## Warning: NAs introduced by coercionc2015 = c2015[!(is.na(c2015$TRAV_SP)),]
#Getting rid of missing data
c2015 = c2015 %>%
filter_all(~!is.na(.))
c2015 = c2015 %>%
filter_all(~!(.=="Unknown"))
c2015 = c2015 %>%
filter_all(~!(.=="Not Rep"))
c2015 = c2015 %>%
filter_all(~!(.=="Not Reported"))
c2015 = c2015 %>%
filter_all(~!(.==str_detect(.,"Not Rep")))
c2015 = c2015 %>%
filter_all(~!(.==str_detect(.,"Unknown")))
c2015 = c2015 %>%
filter(SEAT_POS == 'Front Seat, Left Side')1. Continue with Question 15 of Assignment 5. We want to add a moving/transition variable to the grapth using the gganimate package. Do the follows to achieve that
Install and load the gganimate package
Group the data by SEX, INJ_SEV and MONTH then calculate the mean travel speed
Reuse the code and 15 and adding transition_states(MONTH) to add the MONTH variable
Adding a title using labs(title = ‘MONTH = {closest_state}’)
cum_mean <- mean(c2015$TRAV_SP)
cum_sd <- sd(c2015$TRAV_SP)
gph15 <- c2015 %>%
group_by(MONTH) %>%
summarize(spd_z = round(((mean(TRAV_SP,na.rm=T)- cum_mean) / cum_sd),2)) %>%
mutate(type = ifelse(spd_z > 0 , "above", "below")) %>%
arrange(spd_z)
ggplot(gph15, aes(x=reorder(MONTH,spd_z), y=spd_z, label = spd_z)) +
geom_bar(stat='identity', aes(fill = type, width=0.5)) +
scale_fill_manual(name = "Average Speed",
labels = c("Above Average" , "Below Average"),
values = c("above" = "#00ba38", "below" = "#f8766d")) +
labs(subtitle = "Normalized Average Speed per Month",
title = 'MONTH = {closest_state}',
x= "Month",
y= "Normalized Speed") +
coord_flip() +
transition_states(MONTH)## Warning: Ignoring unknown aesthetics: width
2. Plot bar charts of DRINKING filled by SEX with moving (transition) variable MONTH. Can you add the frequency to each bar?
ggplot(c2015, aes(x=MONTH, DRINKING)) +
geom_bar(stat= 'identity', aes(fill= SEX, width= 0.5)) +
labs(subtitle = 'MONTH = {closest_state}',
title = "Drinking by Month per Sex",
x= "Month",
y= "Drinking") +
coord_flip() +
transition_states(MONTH)## Warning: Ignoring unknown aesthetics: width
3. In this question, we work with the household debt and credit data. Do the follows to import the data to R
Download the data at this link. (Notice that if you use read_excel to read this file, you may not have the desired outcome.)
Install the datapasta package so that we can copy and paste the data to R
Open the downloaded file, tab Page 3 Data. Name the first column of the data table as Quarter
Select and copy the data table including the variables
In Rstudio: Tools -> Addins -> Browse Addins -> Select Paste as tribble -> Excute
Don’t forget to name the data
hhd19 <- tibble::tribble(
~Quarter, ~Mortgage, ~HE.Revolving, ~Auto.Loan, ~Credit.Card, ~Student.Loan, ~Other, ~Total,
"03:Q1", 4.94, 0.24, 0.64, 0.69, 0.24, 0.48, 7.23,
"03:Q2", 5.08, 0.26, 0.62, 0.69, 0.24, 0.49, 7.38,
"03:Q3", 5.18, 0.27, 0.68, 0.69, 0.25, 0.48, 7.56,
"03:Q4", 5.66, 0.3, 0.7, 0.7, 0.25, 0.45, 8.07,
"04:Q1", 5.84, 0.33, 0.72, 0.7, 0.26, 0.45, 8.29,
"04:Q2", 5.97, 0.37, 0.74, 0.7, 0.26, 0.42, 8.46,
"04:Q3", 6.21, 0.43, 0.75, 0.71, 0.33, 0.41, 8.83,
"04:Q4", 6.36, 0.47, 0.73, 0.72, 0.35, 0.42, 9.04,
"05:Q1", 6.51, 0.5, 0.73, 0.71, 0.36, 0.39, 9.21,
"05:Q2", 6.7, 0.53, 0.77, 0.72, 0.37, 0.4, 9.49,
"05:Q3", 6.91, 0.54, 0.83, 0.73, 0.38, 0.41, 9.79,
"05:Q4", 7.1, 0.57, 0.79, 0.74, 0.39, 0.42, 10,
"06:Q1", 7.44, 0.58, 0.79, 0.72, 0.43, 0.42, 10.38,
"06:Q2", 7.76, 0.59, 0.8, 0.74, 0.44, 0.42, 10.75,
"06:Q3", 8.05, 0.6, 0.82, 0.75, 0.45, 0.44, 11.11,
"06:Q4", 8.23, 0.6, 0.82, 0.77, 0.48, 0.41, 11.31,
"07:Q1", 8.42, 0.61, 0.79, 0.76, 0.51, 0.4, 11.5,
"07:Q2", 8.71, 0.62, 0.81, 0.8, 0.51, 0.41, 11.85,
"07:Q3", 8.93, 0.63, 0.82, 0.82, 0.53, 0.41, 12.13,
"07:Q4", 9.1, 0.65, 0.82, 0.84, 0.55, 0.42, 12.37,
"08:Q1", 9.23, 0.66, 0.81, 0.84, 0.58, 0.42, 12.54,
"08:Q2", 9.27, 0.68, 0.81, 0.85, 0.59, 0.4, 12.6,
"08:Q3", 9.29, 0.69, 0.81, 0.86, 0.61, 0.41, 12.68,
"08:Q4", 9.26, 0.71, 0.79, 0.87, 0.64, 0.41, 12.67,
"09:Q1", 9.14, 0.71, 0.77, 0.84, 0.66, 0.41, 12.53,
"09:Q2", 9.06, 0.71, 0.74, 0.82, 0.68, 0.39, 12.41,
"09:Q3", 8.94, 0.71, 0.74, 0.81, 0.69, 0.38, 12.28,
"09:Q4", 8.84, 0.71, 0.72, 0.8, 0.72, 0.38, 12.17,
"10:Q1", 8.83, 0.7, 0.7, 0.76, 0.76, 0.36, 12.12,
"10:Q2", 8.7, 0.68, 0.7, 0.74, 0.76, 0.35, 11.94,
"10:Q3", 8.61, 0.67, 0.71, 0.73, 0.78, 0.34, 11.84,
"10:Q4", 8.45, 0.67, 0.71, 0.73, 0.81, 0.34, 11.71,
"11:Q1", 8.54, 0.64, 0.71, 0.7, 0.84, 0.33, 11.75,
"11:Q2", 8.52, 0.62, 0.71, 0.69, 0.85, 0.33, 11.73,
"11:Q3", 8.4, 0.64, 0.73, 0.69, 0.87, 0.33, 11.66,
"11:Q4", 8.27, 0.63, 0.73, 0.7, 0.87, 0.33, 11.54,
"12:Q1", 8.19, 0.61, 0.74, 0.68, 0.9, 0.32, 11.44,
"12:Q2", 8.15, 0.59, 0.75, 0.67, 0.91, 0.31, 11.38,
"12:Q3", 8.03, 0.57, 0.77, 0.67, 0.96, 0.31, 11.31,
"12:Q4", 8.03, 0.56, 0.78, 0.68, 0.97, 0.32, 11.34,
"13:Q1", 7.93, 0.55, 0.79, 0.66, 0.99, 0.31, 11.23,
"13:Q2", 7.84, 0.54, 0.81, 0.67, 0.99, 0.3, 11.15,
"13:Q3", 7.9, 0.54, 0.85, 0.67, 1.03, 0.3, 11.28,
"13:Q4", 8.05, 0.53, 0.86, 0.68, 1.08, 0.32, 11.52,
"14:Q1", 8.17, 0.53, 0.88, 0.66, 1.11, 0.31, 11.65,
"14:Q2", 8.1, 0.52, 0.91, 0.67, 1.12, 0.32, 11.63,
"14:Q3", 8.13, 0.51, 0.93, 0.68, 1.13, 0.33, 11.71,
"14:Q4", 8.17, 0.51, 0.96, 0.7, 1.16, 0.34, 11.83,
"15:Q1", 8.17, 0.51, 0.97, 0.68, 1.19, 0.33, 11.85,
"15:Q2", 8.12, 0.5, 1.01, 0.7, 1.19, 0.34, 11.85,
"15:Q3", 8.26, 0.49, 1.05, 0.71, 1.2, 0.35, 12.07,
"15:Q4", 8.25, 0.49, 1.06, 0.73, 1.23, 0.35, 12.12,
"16:Q1", 8.37, 0.49, 1.07, 0.71, 1.26, 0.35, 12.25,
"16:Q2", 8.36, 0.48, 1.1, 0.73, 1.26, 0.36, 12.29,
"16:Q3", 8.35, 0.47, 1.14, 0.75, 1.28, 0.37, 12.35,
"16:Q4", 8.48, 0.47, 1.16, 0.78, 1.31, 0.38, 12.58,
"17:Q1", 8.63, 0.46, 1.17, 0.76, 1.34, 0.37, 12.73,
"17:Q2", 8.69, 0.45, 1.19, 0.78, 1.34, 0.38, 12.84,
"17:Q3", 8.74, 0.45, 1.21, 0.81, 1.36, 0.39, 12.96,
"17:Q4", 8.88, 0.44, 1.22, 0.83, 1.38, 0.39, 13.15,
"18:Q1", 8.94, 0.44, 1.23, 0.82, 1.41, 0.39, 13.21,
"18:Q2", 9, 0.43, 1.24, 0.83, 1.41, 0.39, 13.29,
"18:Q3", 9.14, 0.42, 1.27, 0.84, 1.44, 0.4, 13.51,
"18:Q4", 9.12, 0.41, 1.27, 0.87, 1.46, 0.41, 13.54,
"19:Q1", 9.24, 0.41, 1.28, 0.85, 1.49, 0.4, 13.67,
"19:Q2", 9.41, 0.4, 1.3, 0.87, 1.48, 0.41, 13.86
)Plot a line plot between Student.Loan and Credit.Card
gph3 <- ggplot(hhd19, aes(x= Student.Loan, y= Credit.Card)) +
geom_line()4. We want to add a moving variable in the graph of 3. The function transition_reveal (link) is great for this. You may tempt to add transition_reveal(Quarter) but notice that transition_reveal does not take the current form of Quarter. Hint: You can create a dummy variable running from 1 to the size of the data and make it the transition variable.
link <- seq(1, length(hhd19$Quarter))
link## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
## [24] 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46
## [47] 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66gph3 +
transition_reveal(link)
5. The Quarter variable is not in the right format (date). Create the date column where the date is the first day of each quarter. Plot the graph of Student.Loan by date. Hint Use: the seq.date functions with the increment being three months.
library(zoo)##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numerichhd19 <- hhd19 %>%
mutate(date = as.Date(as.yearqtr(Quarter, format = "%y:Q%q")))
hhd19## # A tibble: 66 x 9
## Quarter Mortgage HE.Revolving Auto.Loan Credit.Card Student.Loan Other
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 03:Q1 4.94 0.24 0.64 0.69 0.24 0.48
## 2 03:Q2 5.08 0.26 0.62 0.69 0.24 0.49
## 3 03:Q3 5.18 0.27 0.68 0.69 0.25 0.48
## 4 03:Q4 5.66 0.3 0.7 0.7 0.25 0.45
## 5 04:Q1 5.84 0.33 0.72 0.7 0.26 0.45
## 6 04:Q2 5.97 0.37 0.74 0.7 0.26 0.42
## 7 04:Q3 6.21 0.43 0.75 0.71 0.33 0.41
## 8 04:Q4 6.36 0.47 0.73 0.72 0.35 0.42
## 9 05:Q1 6.51 0.5 0.73 0.71 0.36 0.39
## 10 05:Q2 6.7 0.53 0.77 0.72 0.37 0.4
## # ... with 56 more rows, and 2 more variables: Total <dbl>, date <date>gph5 <- ggplot(hhd19, aes(x= date, y=Student.Loan)) +
geom_line()
gph5
6. Add transition_reveal(date) to the plot in Question 5. to reveal the graph by quarters.
gph6 <- gph5 +
transition_reveal(seq.Date(min(hhd19$date) , max(hhd19$date), by= "quarter"))
gph6
7. Use geom_point and geom_text to plot the moving point and the value of the moving points. Hint: geom_point()+ geom_text(aes(label=Student.Loan)) should work.
gph6 +
geom_point() +
geom_text(aes(label = Student.Loan))
8. Include the graphs of other debts to the plot in Question 7, revealing them by date/quarter and differentiating them by colors. Hint: you may want to change the data from long to wide using the gather function.
hhd19_l <- pivot_longer(hhd19, cols = c(Mortgage, HE.Revolving, Auto.Loan, Credit.Card, Student.Loan, Other, Total))
hhd19_l## # A tibble: 462 x 4
## Quarter date name value
## <chr> <date> <chr> <dbl>
## 1 03:Q1 2003-01-01 Mortgage 4.94
## 2 03:Q1 2003-01-01 HE.Revolving 0.24
## 3 03:Q1 2003-01-01 Auto.Loan 0.64
## 4 03:Q1 2003-01-01 Credit.Card 0.69
## 5 03:Q1 2003-01-01 Student.Loan 0.24
## 6 03:Q1 2003-01-01 Other 0.48
## 7 03:Q1 2003-01-01 Total 7.23
## 8 03:Q2 2003-04-01 Mortgage 5.08
## 9 03:Q2 2003-04-01 HE.Revolving 0.26
## 10 03:Q2 2003-04-01 Auto.Loan 0.62
## # ... with 452 more rowsggplot(hhd19_l, aes(x=date, y= value, color = name)) +
geom_line() +
transition_reveal(date) +
geom_point() +
geom_text(aes(label = value))
9. What is the debt that most correlated with the Total debt. Plot the graph of this debt and the total together revealing by years, differentiation by colors. Plot the remaining debt together in another plot, revealing by years, differentiating by colors. Give a comment on the plots. Label and put captions to the plots.
# Testing for highest correlations with Total
library(ggcorrplot)
hhd19_n <- hhd19 %>%
select_if(is.numeric)
corr <- round(cor(hhd19_n),1)
ggcorrplot(corr, hc.order = T,
type = "lower",
lab = T,
lab_size = 3,
method = "circle",
colors = c("tomato2", "white", "springgreen3"),
title = "Correlogram of hhd19",
ggtheme = theme_bw)
hhd19_l_M <- pivot_longer(hhd19, cols = c(Mortgage, Total))
hhd19_l_M <- hhd19_l_M %>%
select(date, name, value)
hhd19_O <- hhd19 %>%
mutate(Non_Mort = Total - Mortgage)
ggplot(hhd19_l_M, aes(x= date, y=value, fill = name)) +
geom_area() +
labs(title = 'Mortgage and Total Debt from 2003 to 2019',
x = 'Time',
y = 'Trillions of Debt')
ggplot(hhd19_O, aes(x=date, y=Non_Mort)) +
geom_col(fill = "dark green", alpha = 0.8)+
labs(title = 'Other Debt from 2003 to 2019',
x = 'Time',
y = 'Trillions of Debt')
10. Use transition_reveal and transition_states to explore the data of the U.S Economy. Plot three animated plots.
def <- tibble::tribble(
~Year, ~Deficit,
1962, -1.2,
1963, -0.8,
1964, -0.9,
1965, -0.2,
1966, -0.473,
1967, -1.031,
1968, -2.798,
1969, 0.33,
1970, -0.271,
1971, -2.058,
1972, -1.917,
1973, -1.099,
1974, -0.413,
1975, -3.306,
1976, -4.118,
1977, -2.645,
1978, -2.598,
1979, -1.585,
1980, -2.64,
1981, -2.516,
1982, -3.862,
1983, -5.868,
1984, -4.689,
1985, -4.972,
1986, -4.877,
1987, -3.131,
1988, -3.01,
1989, -2.74,
1990, -3.737,
1991, -4.406,
1992, -4.512,
1993, -3.754,
1994, -2.823,
1995, -2.162,
1996, -1.347,
1997, -0.258,
1998, 0.774,
1999, 1.321,
2000, 2.328,
2001, 1.214,
2002, -1.45,
2003, -3.332,
2004, -3.414,
2005, -2.47,
2006, -1.814,
2007, -1.122,
2008, -3.108,
2009, -9.8,
2010, -8.747,
2011, -8.45,
2012, -6.782,
2013, -4.114,
2014, -2.814,
2015, -2.449,
2016, -3.176,
2017, -3.469,
2018, -3.8496
)
ggplot(def, aes(x= Year, y=Deficit)) +
geom_line() +
labs(title = 'US Deficit',
subtitle = 'From 1962 to 2018',
x = 'Time',
y = 'Deficit (100 billions)') +
transition_reveal(Year)
unemp <-tibble::tribble(
~Date, ~Less.than.HS, ~HS, ~Less.than.Bach, ~College,
"Jan-2000", 6.4, 3.4, 2.6, 1.8,
"Feb-2000", 6, 3.4, 3, 1.6,
"Mar-2000", 6.6, NA, 2.8, 1.7,
"Apr-2000", 6.2, 3.3, 2.6, 1.5,
"May-2000", 6.9, 3.4, 2.6, 1.6,
"Jun-2000", 6.3, 3.4, 2.7, 1.6,
"Jul-2000", 6.4, 3.4, 2.8, 1.7,
"Aug-2000", 6.2, 3.7, 2.8, 1.9,
"Sep-2000", 6.2, 3.5, 2.6, 1.9,
"Oct-2000", 6.4, 3.6, 2.4, 1.6,
"Nov-2000", 6.5, 3.5, 2.6, 1.5,
"Dec-2000", 5.9, 3.5, 2.7, 1.5,
"Jan-2001", 6.7, 3.8, 3, 1.6,
"Feb-2001", 7.5, 3.7, 2.8, 1.6,
"Mar-2001", 6.8, 3.8, 2.7, 2,
"Apr-2001", 6.8, 3.7, 2.9, 2.1,
"May-2001", 6.6, 3.9, 3, 2.1,
"Jun-2001", 6.9, 3.8, 3, 2.2,
"Jul-2001", 6.8, 4.1, 3, 2.2,
"Aug-2001", 7.2, 4.4, 3.3, 2.3,
"Sep-2001", 7.7, 4.4, 3.4, 2.5,
"Oct-2001", 7.6, 4.7, 4.1, 2.6,
"Nov-2001", 8, 5, 4.2, 2.9,
"Dec-2001", 8.3, 4.9, 4.2, 3,
"Jan-2002", 8.2, 5.3, 4.3, 2.9,
"Feb-2002", 8.3, 5.3, 4.2, 2.9,
"Mar-2002", 8.1, 5.4, 4.3, 2.8,
"Apr-2002", 9.1, 5.5, 4.6, 2.9,
"May-2002", 8.3, 5.5, 4.8, 2.9,
"Jun-2002", 7.8, 5.4, 4.6, 3,
"Jul-2002", 8.6, 5.1, 4.4, 3,
"Aug-2002", 8.4, 5.2, 4.4, 2.8,
"Sep-2002", 7.8, 5, 4.6, 2.9,
"Oct-2002", 8.7, 4.9, 4.6, 3,
"Nov-2002", 9, 5.2, 4.7, 2.9,
"Dec-2002", 8.9, 5.3, 4.9, 2.9,
"Jan-2003", 8.8, 5.2, 4.7, 3,
"Feb-2003", 8.9, 5.4, 4.7, 3,
"Mar-2003", 8.6, 5.5, 4.8, 3.1,
"Apr-2003", 8.5, 5.7, 4.8, 3,
"May-2003", 9, 5.6, 4.9, 3,
"Jun-2003", 9.4, 5.7, 4.9, 3.1,
"Jul-2003", 8.8, 5.4, 5, 3.1,
"Aug-2003", 9.3, 5.4, 4.9, 3.2,
"Sep-2003", 8.6, 5.3, 4.9, 3.2,
"Oct-2003", 9, 5.5, 4.8, 3.1,
"Nov-2003", 8.7, 5.3, 4.7, 3.1,
"Dec-2003", 7.9, 5.4, 4.4, 3,
"Jan-2004", 9.1, 4.9, 4.5, 2.9,
"Feb-2004", 8.6, 5, 4.3, 2.9,
"Mar-2004", 8.8, 5.3, 4.7, 2.9,
"Apr-2004", 8.6, 5.3, 4.1, 2.9,
"May-2004", 8.8, 5.1, 4, 2.9,
"Jun-2004", 8.7, 5.2, 4.1, 2.7,
"Jul-2004", 8.2, 5, 4.2, 2.6,
"Aug-2004", 8.1, 4.9, 4.1, 2.7,
"Sep-2004", 8.7, 4.7, 4.1, 2.6,
"Oct-2004", 8.4, 4.8, 4.2, 2.5,
"Nov-2004", 8, 4.8, 4.3, 2.5,
"Dec-2004", 8.1, 4.8, 4.2, 2.5,
"Jan-2005", 7.7, 4.7, 4.1, 2.4,
"Feb-2005", 7.8, 4.9, 4.2, 2.4,
"Mar-2005", 7.8, 4.7, 3.9, 2.4,
"Apr-2005", 8.2, 4.4, 3.9, 2.4,
"May-2005", 7.8, 4.5, 3.9, 2.4,
"Jun-2005", 6.9, 4.8, 3.9, 2.3,
"Jul-2005", 7.5, 4.8, 3.7, 2.3,
"Aug-2005", 7.5, 4.7, 3.5, 2.1,
"Sep-2005", 8.2, 4.9, 3.7, 2.3,
"Oct-2005", 7.3, 4.8, 3.8, 2.3,
"Nov-2005", 7.4, 4.8, 3.9, 2.3,
"Dec-2005", 7.4, 4.5, 3.9, 2.1,
"Jan-2006", 7, 4.4, 3.6, 2.1,
"Feb-2006", 7.1, 4.4, 3.6, 2.2,
"Mar-2006", 6.9, 4.1, 3.7, 2.2,
"Apr-2006", 6.9, 4.4, 3.8, 2.2,
"May-2006", 7, 4.3, 3.8, 2.1,
"Jun-2006", 7.1, 4.1, 3.5, 2.1,
"Jul-2006", 7.1, 4.5, 3.6, 2,
"Aug-2006", 6.8, 4.6, 3.5, 1.8,
"Sep-2006", 6.4, 4.1, 3.6, 2.1,
"Oct-2006", 5.8, 4.1, 3.4, 1.9,
"Nov-2006", 6.6, 4.3, 3.4, 1.8,
"Dec-2006", 6.7, 4.3, 3.4, 1.8,
"Jan-2007", 6.9, 4.3, 3.8, 2.1,
"Feb-2007", 7.3, 4.3, 3.6, 1.9,
"Mar-2007", 6.9, 4, 3.6, 1.8,
"Apr-2007", 7.1, 4.1, 3.6, 1.9,
"May-2007", 6.6, 4.4, 3.4, 2,
"Jun-2007", 6.7, 4.1, 3.5, 2,
"Jul-2007", 7.3, 4.6, 3.6, 2.1,
"Aug-2007", 6.5, 4.4, 3.6, 2.1,
"Sep-2007", 7.6, 4.5, 3.4, 2,
"Oct-2007", 7.3, 4.6, 3.5, 2.1,
"Nov-2007", 7.7, 4.5, 3.3, 2.2,
"Dec-2007", 7.7, 4.7, 3.8, 2.1,
"Jan-2008", 7.7, 4.7, 3.7, 2.1,
"Feb-2008", 7.4, 4.7, 3.8, 2,
"Mar-2008", 8.4, 5.1, 3.9, 2.1,
"Apr-2008", 7.7, 5, 4, 2.1,
"May-2008", 8.1, 5, 4.3, 2.2,
"Jun-2008", 8.7, 5.1, 4.3, 2.4,
"Jul-2008", 8.6, 5.4, 4.6, 2.5,
"Aug-2008", 9.7, 5.8, 4.9, 2.8,
"Sep-2008", 9.8, 6.2, 5, 2.6,
"Oct-2008", 10.3, 6.4, 5.2, 3.1,
"Nov-2008", 10.8, 7, 5.5, 3.2,
"Dec-2008", 11.1, 7.8, 5.7, 3.6,
"Jan-2009", 12.4, 8.2, 6.5, 3.9,
"Feb-2009", 13.2, 8.5, 7.3, 4.1,
"Mar-2009", 14, 9.2, 7.5, 4.3,
"Apr-2009", 14.9, 9.5, 7.7, 4.4,
"May-2009", 15.2, 10, 7.8, 4.8,
"Jun-2009", 15.6, 9.7, 8.1, 4.8,
"Jul-2009", 15.3, 9.5, 8, 4.8,
"Aug-2009", 15.6, 9.7, 8.1, 4.8,
"Sep-2009", 14.9, 10.6, 8.3, 5,
"Oct-2009", 15.2, 11, 8.7, 4.7,
"Nov-2009", 14.7, 10.3, 8.8, 4.8,
"Dec-2009", 15, 10.6, 8.7, 4.9,
"Jan-2010", 15.3, 10.2, 8.6, 4.9,
"Feb-2010", 15.8, 10.7, 8.1, 4.9,
"Mar-2010", 14.9, 11, 8.4, 4.9,
"Apr-2010", 14.7, 10.8, 8.4, 4.8,
"May-2010", 14.6, 10.9, 8.2, 4.6,
"Jun-2010", 14.2, 10.6, 8.1, 4.4,
"Jul-2010", 13.5, 10, 8.3, 4.5,
"Aug-2010", 14.1, 10, 8.7, 4.6,
"Sep-2010", 15.6, 9.7, 8.9, 4.5,
"Oct-2010", 15, 9.8, 8.2, 4.6,
"Nov-2010", 15.4, 10, 8.6, 5,
"Dec-2010", 15, 9.8, 8.1, 4.8,
"Jan-2011", 14.3, 9.5, 8.1, 4.3,
"Feb-2011", 14, 9.7, 7.9, 4.3,
"Mar-2011", 14.1, 9.7, 7.5, 4.4,
"Apr-2011", 14.7, 9.9, 7.5, 4.5,
"May-2011", 14.5, 9.6, 7.8, 4.5,
"Jun-2011", 14.4, 10, 8.3, 4.3,
"Jul-2011", 14.5, 9.1, 8.3, 4.2,
"Aug-2011", 14.1, 9.3, 8.3, 4.2,
"Sep-2011", 14.3, 9.4, 8.5, 4.2,
"Oct-2011", 13.5, 9.4, 8.1, 4.3,
"Nov-2011", 12.8, 8.8, 7.7, 4.4,
"Dec-2011", 13.7, 8.7, 7.6, 4.1,
"Jan-2012", 13, 8.5, 7.1, 4.3,
"Feb-2012", 13.1, 8.3, 7.2, 4.2,
"Mar-2012", 12.8, 8.1, 7.5, 4.1,
"Apr-2012", 12.5, 8, 7.6, 4,
"May-2012", 12.9, 8.3, 7.7, 3.9,
"Jun-2012", 12.6, 8.6, 7.3, 4,
"Jul-2012", 12.4, 8.5, 7.2, 4.1,
"Aug-2012", 11.8, 8.6, 6.7, 4.1,
"Sep-2012", 11.7, 8.5, 6.6, 4,
"Oct-2012", 12.1, 8.3, 6.9, 3.7,
"Nov-2012", 12, 8, 6.5, 3.9,
"Dec-2012", 11.8, 8, 6.9, 4,
"Jan-2013", 12, 8.1, 6.9, 3.8,
"Feb-2013", 11.3, 7.9, 6.5, 3.9,
"Mar-2013", 11.1, 7.7, 6.3, 3.8,
"Apr-2013", 11.6, 7.5, 6.4, 3.9,
"May-2013", 11, 7.3, 6.5, 3.9,
"Jun-2013", 10.7, 7.7, 6.5, 3.8,
"Jul-2013", 10.8, 7.5, 6.1, 3.8,
"Aug-2013", 11.1, 7.4, 6.1, 3.4,
"Sep-2013", 10.5, 7.6, 6.1, 3.7,
"Oct-2013", 10.9, 7.2, 6.4, 3.7,
"Nov-2013", 10.7, 7.2, 6.4, 3.4,
"Dec-2013", 9.8, 7, 6.2, 3.3,
"Jan-2014", 9.4, 6.5, 5.9, 3.3,
"Feb-2014", 9.8, 6.4, 6, 3.4,
"Mar-2014", 9.4, 6.3, 6, 3.4,
"Apr-2014", 8.7, 6.2, 5.7, 3.3,
"May-2014", 9.2, 6.5, 5.5, 3.2,
"Jun-2014", 9.2, 5.9, 5.2, 3.3,
"Jul-2014", 9.5, 6.1, 5.3, 3.1,
"Aug-2014", 9.2, 6.1, 5.3, 3.2,
"Sep-2014", 8.5, 5.4, 5.4, 2.9,
"Oct-2014", 8.1, 5.7, 4.9, 3,
"Nov-2014", 8.6, 5.7, 4.9, 3.2,
"Dec-2014", 8.6, 5.3, 5, 2.8,
"Jan-2015", 8.3, 5.4, 5.2, 2.8,
"Feb-2015", 8.2, 5.4, 5, 2.7,
"Mar-2015", 8.6, 5.3, 4.8, 2.4,
"Apr-2015", 8.5, 5.4, 4.7, 2.7,
"May-2015", 8.7, 5.8, 4.4, 2.8,
"Jun-2015", 8.2, 5.4, 4.2, 2.5,
"Jul-2015", 8.3, 5.5, 4.3, 2.5,
"Aug-2015", 7.9, 5.5, 4.3, 2.4,
"Sep-2015", 7.9, 5.3, 4.3, 2.5,
"Oct-2015", 7.6, 5.2, 4.4, 2.5,
"Nov-2015", 6.8, 5.5, 4.4, 2.6,
"Dec-2015", 6.5, 5.6, 4.2, 2.5,
"Jan-2016", 7.1, 5.2, 4.2, 2.5,
"Feb-2016", 7, 5.2, 4.2, 2.5,
"Mar-2016", 7.4, 5.3, 4.2, 2.6,
"Apr-2016", 7.6, 5.4, 4.2, 2.4,
"May-2016", 7.5, 5.1, 3.8, 2.5,
"Jun-2016", 7.6, 5.1, 4.2, 2.5,
"Jul-2016", 6.4, 5.1, 4.2, 2.5,
"Aug-2016", 7.4, 5, 4.2, 2.6,
"Sep-2016", 8.5, 5.3, 4.2, 2.5,
"Oct-2016", 7.5, 5.5, 3.9, 2.6,
"Nov-2016", 7.8, 5, 3.9, 2.4,
"Dec-2016", 7.6, 5.1, 3.8, 2.5,
"Jan-2017", 7.4, 5.2, 3.8, 2.5,
"Feb-2017", 7.6, 4.9, 4, 2.4,
"Mar-2017", 6.6, 4.9, 3.7, 2.4,
"Apr-2017", 6.4, 4.6, 3.7, 2.4,
"May-2017", 6.3, 4.7, 4, 2.3,
"Jun-2017", 6.5, 4.6, 3.8, 2.3,
"Jul-2017", 7, 4.5, 3.8, 2.3,
"Aug-2017", 6.1, 5, 3.7, 2.4,
"Sep-2017", 6.7, 4.4, 3.6, 2.3,
"Oct-2017", 6, 4.3, 3.7, 2.1,
"Nov-2017", 5.2, 4.4, 3.6, 2.1,
"Dec-2017", 6.3, 4.2, 3.6, 2.1,
"Jan-2018", 5.5, 4.4, 3.4, 2.2,
"Feb-2018", 5.6, 4.4, 3.5, 2.2,
"Mar-2018", 5.6, 4.3, 3.5, 2.2,
"Apr-2018", 5.8, 4.3, 3.4, 2.1,
"May-2018", 5.5, 3.9, 3.2, 2,
"Jun-2018", 5.6, 4.1, 3.3, 2.3,
"Jul-2018", 5, 4, 3.2, 2.2,
"Aug-2018", 5.7, 3.9, 3.5, 2,
"Sep-2018", 5.6, 3.7, 3.2, 2,
"Oct-2018", 5.9, 4, 3, 2,
"Nov-2018", 5.6, 3.5, 3.1, 2.2,
"Dec-2018", 5.8, 3.8, 3.3, 2.1,
"Jan-2019", 5.7, 3.8, 3.4, 2.4,
"Feb-2019", 5.3, 3.8, 3.2, 2.2,
"Mar-2019", 5.9, 3.7, 3.4, 2,
"Apr-2019", 5.4, 3.5, 3.1, 2.1,
"May-2019", 5.4, 3.5, 2.8, 2.1,
"Jun-2019", 5.3, 3.9, 3, 2.1,
"Jul-2019", 5.1, 3.6, 3.2, 2.2,
"Aug-2019", 5.4, 3.6, 3.1, 2.1,
"Sep-2019", 4.8, 3.6, 2.9, 2
)
unemp_l <- pivot_longer(unemp, cols = c(Less.than.HS, HS, Less.than.Bach, College))
unemp_l <- unemp_l %>%
mutate(Date = as.Date(as.yearmon(Date, format = "%b-%Y")))
unemp_l## # A tibble: 948 x 3
## Date name value
## <date> <chr> <dbl>
## 1 2000-01-01 Less.than.HS 6.4
## 2 2000-01-01 HS 3.4
## 3 2000-01-01 Less.than.Bach 2.6
## 4 2000-01-01 College 1.8
## 5 2000-02-01 Less.than.HS 6
## 6 2000-02-01 HS 3.4
## 7 2000-02-01 Less.than.Bach 3
## 8 2000-02-01 College 1.6
## 9 2000-03-01 Less.than.HS 6.6
## 10 2000-03-01 HS NA
## # ... with 938 more rowsggplot(unemp_l, aes(x=Date, y = value, color = name)) +
geom_line() +
transition_reveal(Date) +
labs(
title= 'Unemployment Rat by Education Level',
subtitle= 'From 2000 to 2019',
x= 'Time',
y= 'Unemployment Rate'
)## Warning: Removed 1 rows containing missing values (geom_path).
## Warning: Removed 1 rows containing missing values (geom_path).
last <- tibble::tribble(
~Date, ~Quit.Rate, ~Job.Openings,
"Dec-2000", NA, 0.864,
"Jan-2001", NA, 0.89,
"Feb-2001", 2.3, 0.822,
"Mar-2001", 2.4, 0.765,
"Apr-2001", 2.3, 0.758,
"May-2001", 2.3, 0.723,
"Jun-2001", 2.3, 0.675,
"Jul-2001", 2.2, 0.695,
"Aug-2001", 2.2, 0.574,
"Sep-2001", 2.2, 0.566,
"Oct-2001", 2.1, 0.485,
"Nov-2001", 2, 0.456,
"Dec-2001", 2, 0.427,
"Jan-2002", 2, 0.462,
"Feb-2002", 2, 0.411,
"Mar-2002", 2, 0.43,
"Apr-2002", 2, 0.416,
"May-2002", 1.9, 0.418,
"Jun-2002", 1.9, 0.407,
"Jul-2002", 1.9, 0.413,
"Aug-2002", 2, 0.425,
"Sep-2002", 2, 0.399,
"Oct-2002", 1.9, 0.422,
"Nov-2002", 1.9, 0.4,
"Dec-2002", 1.9, 0.352,
"Jan-2003", 1.9, 0.411,
"Feb-2003", 1.9, 0.369,
"Mar-2003", 1.9, 0.357,
"Apr-2003", 1.8, 0.363,
"May-2003", 1.8, 0.372,
"Jun-2003", 1.8, 0.367,
"Jul-2003", 1.8, 0.341,
"Aug-2003", 1.8, 0.36,
"Sep-2003", 1.8, 0.345,
"Oct-2003", 1.8, 0.381,
"Nov-2003", 1.8, 0.375,
"Dec-2003", 1.9, 0.394,
"Jan-2004", 1.8, 0.414,
"Feb-2004", 1.9, 0.426,
"Mar-2004", 1.9, 0.411,
"Apr-2004", 1.9, 0.442,
"May-2004", 1.9, 0.458,
"Jun-2004", 1.9, 0.404,
"Jul-2004", 1.9, 0.479,
"Aug-2004", 2, 0.445,
"Sep-2004", 2, 0.483,
"Oct-2004", 1.9, 0.492,
"Nov-2004", 2, 0.424,
"Dec-2004", 2, 0.496,
"Jan-2005", 2.1, 0.492,
"Feb-2005", 2, 0.489,
"Mar-2005", 2.1, 0.519,
"Apr-2005", 2.1, 0.557,
"May-2005", 2.1, 0.502,
"Jun-2005", 2.1, 0.541,
"Jul-2005", 2.1, 0.586,
"Aug-2005", 2.1, 0.566,
"Sep-2005", 2.2, 0.579,
"Oct-2005", 2.2, 0.566,
"Nov-2005", 2.2, 0.544,
"Dec-2005", 2.1, 0.57,
"Jan-2006", 2.1, 0.623,
"Feb-2006", 2.2, 0.593,
"Mar-2006", 2.2, 0.665,
"Apr-2006", 2.1, 0.69,
"May-2006", 2.1, 0.645,
"Jun-2006", 2.1, 0.661,
"Jul-2006", 2.2, 0.623,
"Aug-2006", 2.2, 0.668,
"Sep-2006", 2.2, 0.698,
"Oct-2006", 2.2, 0.69,
"Nov-2006", 2.2, 0.659,
"Dec-2006", 2.2, 0.661,
"Jan-2007", 2.2, 0.668,
"Feb-2007", 2.2, 0.667,
"Mar-2007", 2.2, 0.732,
"Apr-2007", 2.1, 0.704,
"May-2007", 2.2, 0.695,
"Jun-2007", 2.1, 0.7,
"Jul-2007", 2.1, 0.653,
"Aug-2007", 2.1, 0.649,
"Sep-2007", 2.1, 0.653,
"Oct-2007", 2.1, 0.646,
"Nov-2007", 2, 0.627,
"Dec-2007", 2, 0.576,
"Jan-2008", 2, 0.599,
"Feb-2008", 2.1, 0.565,
"Mar-2008", 2, 0.538,
"Apr-2008", 2, 0.54,
"May-2008", 2, 0.505,
"Jun-2008", 2, 0.449,
"Jul-2008", 1.9, 0.425,
"Aug-2008", 1.8, 0.393,
"Sep-2008", 1.8, 0.341,
"Oct-2008", 1.8, 0.338,
"Nov-2008", 1.7, 0.3,
"Dec-2008", 1.6, 0.271,
"Jan-2009", 1.5, 0.226,
"Feb-2009", 1.5, 0.22,
"Mar-2009", 1.5, 0.188,
"Apr-2009", 1.4, 0.169,
"May-2009", 1.3, 0.178,
"Jun-2009", 1.3, 0.171,
"Jul-2009", 1.3, 0.155,
"Aug-2009", 1.3, 0.16,
"Sep-2009", 1.3, 0.165,
"Oct-2009", 1.3, 0.158,
"Nov-2009", 1.3, 0.16,
"Dec-2009", 1.4, 0.165,
"Jan-2010", 1.4, 0.188,
"Feb-2010", 1.4, 0.175,
"Mar-2010", 1.4, 0.176,
"Apr-2010", 1.4, 0.21,
"May-2010", 1.4, 0.203,
"Jun-2010", 1.5, 0.194,
"Jul-2010", 1.4, 0.215,
"Aug-2010", 1.4, 0.207,
"Sep-2010", 1.4, 0.2,
"Oct-2010", 1.4, 0.224,
"Nov-2010", 1.4, 0.208,
"Dec-2010", 1.4, 0.207,
"Jan-2011", 1.4, 0.221,
"Feb-2011", 1.5, 0.232,
"Mar-2011", 1.5, 0.237,
"Apr-2011", 1.5, 0.239,
"May-2011", 1.5, 0.231,
"Jun-2011", 1.5, 0.248,
"Jul-2011", 1.5, 0.267,
"Aug-2011", 1.5, 0.243,
"Sep-2011", 1.5, 0.27,
"Oct-2011", 1.5, 0.269,
"Nov-2011", 1.5, 0.262,
"Dec-2011", 1.5, 0.28,
"Jan-2012", 1.5, 0.305,
"Feb-2012", 1.5, 0.281,
"Mar-2012", 1.6, 0.313,
"Apr-2012", 1.6, 0.307,
"May-2012", 1.6, 0.305,
"Jun-2012", 1.6, 0.309,
"Jul-2012", 1.6, 0.299,
"Aug-2012", 1.5, 0.308,
"Sep-2012", 1.5, 0.319,
"Oct-2012", 1.5, 0.314,
"Nov-2012", 1.5, 0.315,
"Dec-2012", 1.5, 0.312,
"Jan-2013", 1.6, 0.314,
"Feb-2013", 1.6, 0.333,
"Mar-2013", 1.7, 0.349,
"Apr-2013", 1.7, 0.347,
"May-2013", 1.6, 0.358,
"Jun-2013", 1.6, 0.355,
"Jul-2013", 1.6, 0.348,
"Aug-2013", 1.7, 0.365,
"Sep-2013", 1.7, 0.364,
"Oct-2013", 1.7, 0.383,
"Nov-2013", 1.7, 0.371,
"Dec-2013", 1.7, 0.384,
"Jan-2014", 1.7, 0.405,
"Feb-2014", 1.7, 0.42,
"Mar-2014", 1.7, 0.423,
"Apr-2014", 1.8, 0.481,
"May-2014", 1.8, 0.484,
"Jun-2014", 1.8, 0.528,
"Jul-2014", 1.8, 0.512,
"Aug-2014", 1.8, 0.561,
"Sep-2014", 1.9, 0.526,
"Oct-2014", 1.9, 0.564,
"Nov-2014", 1.9, 0.519,
"Dec-2014", 1.8, 0.571,
"Jan-2015", 1.9, 0.604,
"Feb-2015", 1.9, 0.628,
"Mar-2015", 2, 0.614,
"Apr-2015", 1.9, 0.672,
"May-2015", 1.9, 0.634,
"Jun-2015", 1.9, 0.638,
"Jul-2015", 1.9, 0.75,
"Aug-2015", 2, 0.69,
"Sep-2015", 2, 0.691,
"Oct-2015", 2, 0.738,
"Nov-2015", 2, 0.696,
"Dec-2015", 2, 0.717,
"Jan-2016", 2, 0.771,
"Feb-2016", 2.1, 0.74,
"Mar-2016", 2, 0.775,
"Apr-2016", 2.1, 0.741,
"May-2016", 2.1, 0.757,
"Jun-2016", 2.1, 0.741,
"Jul-2016", 2.1, 0.791,
"Aug-2016", 2.1, 0.732,
"Sep-2016", 2.1, 0.735,
"Oct-2016", 2.1, 0.718,
"Nov-2016", 2.1, 0.784,
"Dec-2016", 2.1, 0.777,
"Jan-2017", 2.1, 0.743,
"Feb-2017", 2.1, 0.79,
"Mar-2017", 2.2, 0.826,
"Apr-2017", 2.1, 0.877,
"May-2017", 2.1, 0.833,
"Jun-2017", 2.1, 0.917,
"Jul-2017", 2.1, 0.917,
"Aug-2017", 2.1, 0.893,
"Sep-2017", 2.1, 0.921,
"Oct-2017", 2.2, 0.972,
"Nov-2017", 2.2, 0.922,
"Dec-2017", 2.2, 0.944,
"Jan-2018", 2.1, 0.992,
"Feb-2018", 2.1, 0.977,
"Mar-2018", 2.1, 1.063,
"Apr-2018", 2.2, 1.122,
"May-2018", 2.2, 1.163,
"Jun-2018", 2.2, 1.131,
"Jul-2018", 2.3, 1.192,
"Aug-2018", 2.3, 1.185,
"Sep-2018", 2.3, 1.235,
"Oct-2018", 2.3, 1.242,
"Nov-2018", 2.3, 1.267,
"Dec-2018", 2.3, 1.188,
"Jan-2019", 2.3, 1.167,
"Feb-2019", 2.3, 1.145,
"Mar-2019", 2.3, 1.203,
"Apr-2019", 2.3, 1.266,
"May-2019", 2.3, 1.254,
"Jun-2019", 2.3, 1.213,
"Jul-2019", 2.3, 1.183,
"Aug-2019", 2.3, 1.167
)
last_l <- pivot_longer(last, cols = c(Quit.Rate, Job.Openings))
last_l <- last_l %>%
mutate(Date = as.Date(as.yearmon(Date, format = "%b-%Y")))
last_l## # A tibble: 450 x 3
## Date name value
## <date> <chr> <dbl>
## 1 2000-12-01 Quit.Rate NA
## 2 2000-12-01 Job.Openings 0.864
## 3 2001-01-01 Quit.Rate NA
## 4 2001-01-01 Job.Openings 0.89
## 5 2001-02-01 Quit.Rate 2.3
## 6 2001-02-01 Job.Openings 0.822
## 7 2001-03-01 Quit.Rate 2.4
## 8 2001-03-01 Job.Openings 0.765
## 9 2001-04-01 Quit.Rate 2.3
## 10 2001-04-01 Job.Openings 0.758
## # ... with 440 more rowsggplot(last_l, aes(x= Date, y=value, color=name)) +
geom_line()+
transition_reveal(Date)## Warning: Removed 3 rows containing missing values (geom_path).
## Warning: Removed 3 rows containing missing values (geom_path).## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).